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TFIDF-FL: Localizing Faults Using Term Frequency-Inverse Document Frequency and Deep Learning Zhuo ZHANG Yan LEI Jianjun XU Xiaoguang MAO Xi CHANG Publication IEICE TRANSACTIONS on Information and Systems Vol.E102-D No.9 pp.1860-1864 Publication Date: 2019/09/01 Publicized: 2019/05/27 Online ISSN: 1745-1361 DOI: 10.1587/transinf.2018EDL8237 Type of Manuscript: LETTER Category: Software Engineering Keyword: debugging, fault localization, term frequency, inverse document frequency, deep learning, Full Text: PDF>>
Summary: Existing fault localization based on neural networks utilize the information of whether a statement is executed or not executed to identify suspicious statements potentially responsible for a failure. However, the information just shows the binary execution states of a statement, and cannot show how important a statement is in executions. Consequently, it may degrade fault localization effectiveness. To address this issue, this paper proposes TFIDF-FL by using term frequency-inverse document frequency to identify a high or low degree of the influence of a statement in an execution. Our empirical results on 8 real-world programs show that TFIDF-FL significantly improves fault localization effectiveness. | |||||
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